DMRS: Long-tailed remote sensing recognition via semantic-aware mixing and diversity experts
Long-tailed class distributions pose a significant challenge in remote sensing scene recognition, where certain scene categories appear far less frequently than others. However, existing long-tailed learning approaches often overlook the unique spatial hierarchies and contextual semantic relationshi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-07-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225002705 |
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| Summary: | Long-tailed class distributions pose a significant challenge in remote sensing scene recognition, where certain scene categories appear far less frequently than others. However, existing long-tailed learning approaches often overlook the unique spatial hierarchies and contextual semantic relationships inherent in remote sensing imagery, limiting their effectiveness in this domain. To address this, we propose Diversity-Mix Remote Sensing (DMRS), a foundation model-based framework designed for long-tailed remote sensing scene recognition. DMRS introduces two key innovations: (1) multi-low-rank adaptation diversity experts, which achieves balanced classification by specializing different experts for different regions of the class distribution, and (2) a semantic-aware mixing strategy, which incorporates textual semantic information typically unused in traditional classification to enhance perception across diverse remote sensing scenes. Extensive experiments on NWPU-RESISC45 and RSD46-WHU datasets demonstrate the effectiveness of DMRS, achieving 6.7% and 2.0% improvements in overall accuracy, respectively, while significantly enhancing the recognition of tail classes. These results highlight the potential of DMRS in tackling long-tail challenges in remote sensing scene classification. The data and codes used in the study are detailed in: https://github.com/wyfhbb/DMRS. |
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| ISSN: | 1569-8432 |